—1354 title: “Graphics and Data
Visualization in R” author: Thomas Girke date: May 19, 2022FUJIFILM X-T30 II output: ioslides_presentation: keep_md: yes logo: ./images/ucr_logo.png widescreen: yes df_print: paged smaller: true subtitle: “Overview of Graphics Environments in R” bibliography: bibtex.bib —

How to Navigate this Slide Show?

This ioslides presentation contains scrollable slides.
Which slides are scrollable, is indicated by a tag at the bottom of the corresponding slides stating:

[ Scroll down to continue ]

The following single character keyboard shortcuts enable alternate display modes of ioslides:
- f: enable fullscreen mode
- w: toggle widescreen mode
- o: enable overview mode
- h: enable code highlight mode
Pressing Esc exits all of these modes. Additional details can be found here.

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

Overview

Graphics in R

Large ecosystem for visualizing data
Fully programmable and highly reproducible publication quality graphics
Full LaTeX, Sweave, knitr and R Markdown support.

Documentation on Graphics in R

General
Interactive graphics
- plotly and iplots
- Open GL (rgl) and rggobi (GGobi)

Graphics Environments

Viewing and savings graphics in R
- On-screen graphics
- postscript, pdf, svg
- jpeg/png/wmf/tiff/…
Four major graphics environments
- Low-level infrastructure
  - R Base Graphics (low- and high-level)
  - grid: Manual, Book
- High-level infrastructure
  - lattice: Manual, Intro, Book
  - ggplot2: Manual, Intro, Book

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

Base Graphics

Overview

Important high-level plotting functions
- plot: generic x-y plotting
- barplot: bar plots
- boxplot: box-and-whisker plot
- hist: histograms
- pie: pie charts
- dotchart: cleveland dot plots
- image, heatmap, contour, persp: functions to generate image-like plots
- qqnorm, qqline, qqplot: distribution comparison plots
- pairs, coplot: display of multivariant data
Help on these functions
- ?myfct
  [ Scroll down to continue ]
- ?plot
- ?par

Preferred Input Data Objects

Matrices and data frames
Vectors
Named vectors

Scatter Plots

Basic scatter plots

Sample data set for subsequent plots

set.seed(1410)
y <- matrix(runif(30), ncol=3, dimnames=list(letters[1:10], LETTERS[1:3]))
y

##            A          B            C
## a 0.26904539 0.47439030 0.4427788756
## b 0.53178658 0.31128960 0.3233293493
## c 0.93379571 0.04576263 0.0004628517
## d 0.14314802 0.12066723 0.4104402000
## e 0.57627063 0.83251909 0.9884746270
## f 0.49001235 0.38298651 0.8235850153
## g 0.66562596 0.70857731 0.7490944304
## h 0.50089252 0.24772695 0.2117313873
## i 0.57033245 0.06044799 0.8776291364
## j 0.04087422 0.85814118 0.1061618729

plot(y[,1], y[,2])

All pairs

pairs(y)

Plot labels

plot(y[,1], y[,2], pch=20, col="red", main="Symbols and Labels")
text(y[,1]+0.03, y[,2], rownames(y))

More examples

Print instead of symbols the row names

plot(y[,1], y[,2], type="n", main="Plot of Labels")
text(y[,1], y[,2], rownames(y))

Usage of important plotting parameters

grid(5, 5, lwd = 2) 
op <- par(mar=c(8,8,8,8), bg="lightblue")
plot(y[,1], y[,2], type="p", col="red", cex.lab=1.2, cex.axis=1.2, 
     cex.main=1.2, cex.sub=1, lwd=4, pch=20, xlab="x label", 
     ylab="y label", main="My Main", sub="My Sub")
par(op)

Important arguments} - mar: specifies the margin sizes around the plotting area in order: c(bottom, left, top, right) - col: color of symbols - pch: type of symbols, samples: example(points) - lwd: size of symbols - cex.*: control font sizes - For details see ?par

Add a regression line to a plot

plot(y[,1], y[,2])
myline <- lm(y[,2]~y[,1]); abline(myline, lwd=2)

summary(myline)

## 
## Call:
## lm(formula = y[, 2] ~ y[, 1])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.40357 -0.17912 -0.04299  0.22147  0.46623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.5764     0.2110   2.732   0.0258 *
## y[, 1]       -0.3647     0.3959  -0.921   0.3839  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3095 on 8 degrees of freedom
## Multiple R-squared:  0.09589,    Adjusted R-squared:  -0.01712 
## F-statistic: 0.8485 on 1 and 8 DF,  p-value: 0.3839

Same plot as above, but on log scale

plot(y[,1], y[,2], log="xy")

Add a mathematical expression to a plot

plot(y[,1], y[,2]); text(y[1,1], y[1,2], 
     expression(sum(frac(1,sqrt(x^2*pi)))), cex=1.3)

Exercise 1

Task 1: Generate scatter plot for first two columns in iris data frame and color dots by its Species column.
Task 2: Use the xlim/ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.

Structure of iris data set:

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Line Plots

Single Data Set

plot(y[,1], type="l", lwd=2, col="blue")

Many Data Sets

Plots line graph for all columns in data frame y. The split.screen function is used in this example in a for loop to overlay several line graphs in the same plot.

split.screen(c(1,1))

## [1] 1

plot(y[,1], ylim=c(0,1), xlab="Measurement", ylab="Intensity", type="l", lwd=2, col=1)
for(i in 2:length(y[1,])) { 
    screen(1, new=FALSE)
    plot(y[,i], ylim=c(0,1), type="l", lwd=2, col=i, xaxt="n", yaxt="n", ylab="", 
             xlab="", main="", bty="n") 
}

close.screen(all=TRUE)

Bar Plots

Basics

barplot(y[1:4,], ylim=c(0, max(y[1:4,])+0.3), beside=TRUE, 
        legend=letters[1:4]) 
text(labels=round(as.vector(as.matrix(y[1:4,])),2), x=seq(1.5, 13, by=1)
     +sort(rep(c(0,1,2), 4)), y=as.vector(as.matrix(y[1:4,]))+0.04)

Error bars

bar <- barplot(m <- rowMeans(y) * 10, ylim=c(0, 10))
stdev <- sd(t(y))
arrows(bar, m, bar, m + stdev, length=0.15, angle = 90)

Mirrored bar plot

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
plot(c(0,12), range(df$y), type = "n")
barplot(height = df$y[df$group == "Above"], add = TRUE, axes = FALSE)
barplot(height = df$y[df$group == "Below"], add = TRUE, axes = FALSE)

Histograms

hist(y, freq=TRUE, breaks=10)

Density Plots}

plot(density(y), col="red")

Pie Charts

pie(y[,1], col=rainbow(length(y[,1]), start=0.1, end=0.8), clockwise=TRUE)
legend("topright", legend=row.names(y), cex=1.3, bty="n", pch=15, pt.cex=1.8, 
col=rainbow(length(y[,1]), start=0.1, end=0.8), ncol=1)

Color Selection Utilities

Default color palette and how to change it

palette()

## [1] "black"   "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710" "gray62"

palette(rainbow(5, start=0.1, end=0.2))
palette()

## [1] "#FF9900" "#FFBF00" "#FFE600" "#F2FF00" "#CCFF00"

palette("default")

The gray function allows to select any type of gray shades by providing values from 0 to 1

gray(seq(0.1, 1, by= 0.2))

## [1] "#1A1A1A" "#4D4D4D" "#808080" "#B3B3B3" "#E6E6E6"

Color gradients with colorpanel function from gplots library

library(gplots)
colorpanel(5, "darkblue", "yellow", "white")

Much more on colors in R see Earl Glynn’s color chart

Arranging Several Plots on Single Page

With par(mfrow=c(nrow, ncol)) one can define how several plots are arranged next to each other.

par(mfrow=c(2,3)) 
for(i in 1:6) plot(1:10)

Arranging Plots with Variable Width

The layout function allows to divide the plotting device into variable numbers of rows and columns with the column-widths and the row-heights specified in the respective arguments.

nf <- layout(matrix(c(1,2,3,3), 2, 2, byrow=TRUE), c(3,7), c(5,5), 
             respect=TRUE)
# layout.show(nf)
for(i in 1:3) barplot(1:10)

Saving Graphics to Files

After the pdf() command all graphs are redirected to file test.pdf. Works for all common formats similarly: jpeg, png, ps, tiff, …

pdf("test.pdf"); plot(1:10, 1:10); dev.off()

Generates Scalable Vector Graphics (SVG) files that can be edited in vector graphics programs, such as InkScape.

svg("test.svg"); plot(1:10, 1:10); dev.off()

Exercise 2

Bar plots

Task 1: Calculate the mean values for the Species components of the first four columns in the iris data set. Organize the results in a matrix where the row names are the unique values from the iris Species column and the column names are the same as in the first four iris columns.
Task 2: Generate two bar plots: one with stacked bars and one with horizontally arranged bars.

Structure of iris data set:

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

Grid Graphics

What is grid?
- Low-level graphics system
- Highly flexible and controllable system
- Does not provide high-level functions
- Intended as development environment for custom plotting functions
- Pre-installed on new R distributions
Documentation and Help
- Manual
- Book

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

lattice Graphics

What is lattice?
- High-level graphics system
- Developed by Deepayan Sarkar
- Implements Trellis graphics system from S-Plus
- Simplifies high-level plotting tasks: arranging complex graphical features
- Syntax similar to R’s base graphics
Documentation and Help
- Manual
- Intro
- Book

Open a list of all functions available in the lattice package

library(lattice) 
library(help=lattice)

[ Scroll down to continue ]

Accessing and changing global parameters:

?lattice.options
?trellis.device

Scatter Plot Sample

library(lattice)
p1 <- xyplot(1:8 ~ 1:8 | rep(LETTERS[1:4], each=2), as.table=TRUE) 
plot(p1)

Line Plot Sample

library(lattice)
p2 <- parallelplot(~iris[1:4] | Species, iris, horizontal.axis = FALSE, 
              layout = c(1, 3, 1))  
plot(p2)

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
References

ggplot2 Graphics

What is ggplot2?
- High-level graphics system developed by Hadley Wickham
- Implements grammar of graphics from Leland Wilkinson
- Streamlines many graphics workflows for complex plots
- Syntax centered around main ggplot function
- Simpler qplot function provides many shortcuts
Documentation and Help

Design Concept of `ggplot2`

Plotting formalized and implemented by the grammar of graphics by Leland Wilkinson and Hadley Wickham [@Wickham2010-nu; @Wilkinson2012-wv; @Wickham2009-aq]. The plotting process in ggplot2 is devided into layers including:

Theme: styles to be used, such as fonts, backgrounds, etc.
Coordinates: the plotting space
Statistics: data models and summaries
Facets: row and column layout of sub-plots
Geometries: shapes used to represent data (e.g. bar or scatter plot)
Aesthetics: the scales onto which the data will be mapped
Data: the actual data to be plotted

`ggplot2` Usage

ggplot function accepts two main arguments
- Data set to be plotted
- Aesthetic mappings provided by aes function
Additional parameters such as geometric objects (e.g. points, lines, bars) are passed on by appending them with + as separator.
List of available geom_* functions see here
Settings of plotting theme can be accessed with the command theme_get() and its settings can be changed with theme().
Preferred input data object
- qplot: data.frame or tibble (support for vector, matrix, ...)
- ggplot: data.frame or tibble
Packages with convenience utilities to create expected inputs
- dplyr (plyr)
- tidyr and reshape2

Usage with `qplot` Function

The syntax of qplot is similar as R’s basic plot function

Arguments
- x: x-coordinates (e.g. col1)
- y: y-coordinates (e.g. col2)
- data: data.frame or tibble with corresponding column names
- xlim, ylim: e.g. xlim=c(0,10)
- log: e.g. log="x" or log="xy"
- main: main title; see ?plotmath for mathematical formula
- xlab, ylab: labels for the x- and y-axes
- color, shape, size
- ...: many arguments accepted by plot function

[ Scroll down to continue ]

`qplot`: scatter plot basics

Create sample data, here 3 vectors: x, y and cat

library(ggplot2)
x <- sample(1:10, 10); y <- sample(1:10, 10); cat <- rep(c("A", "B"), 5)

Simple scatter plot

qplot(x, y, geom="point")

Prints dots with different sizes and colors

qplot(x, y, geom="point", size=x, color=cat, 
      main="Dot Size and Color Relative to Some Values")

Drops legend

qplot(x, y, geom="point", size=x, color=cat) + 
      theme(legend.position = "none")

Plot different shapes

qplot(x, y, geom="point", size=5, shape=cat)

Colored groups

p <- qplot(x, y, geom="point", size=x, color=cat, 
           main="Dot Size and Color Relative to Some Values") + 
           theme(legend.position = "none")
print(p)

Regression line

set.seed(1410)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
p <- qplot(carat, price, data = dsmall) +
           geom_smooth(method="lm")
print(p)

## `geom_smooth()` using formula 'y ~ x'

Local regression curve (loess)

p <- qplot(carat, price, data=dsmall, geom=c("point", "smooth")) 
print(p) # Setting se=FALSE removes error shade

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Usage with `ggplot` Function

More important than qplot to access full functionality of ggplot2
Main arguments
- data set, usually a data.frame or tibble
- aesthetic mappings provided by aes function
General ggplot syntax
- ggplot(data, aes(...)) + geom() + ... + stat() + ...
Layer specifications
- geom(mapping, data, ..., geom, position)
- stat(mapping, data, ..., stat, position)
Additional components
- scales
- coordinates
- facet
  [ Scroll down to continue ]
aes() mappings can be passed on to all components (ggplot, geom, etc.). Effects are global when passed on to ggplot() and local for other components.
- x, y
- color: grouping vector (factor)
- group: grouping vector (factor)

Changing Plotting Themes in `ggplot`

Theme settings can be accessed with theme_get()
Their settings can be changed with theme()

Example how to change background color to white

... + theme(panel.background=element_rect(fill = "white", colour = "black"))

Storing `ggplot` Specifications

Plots and layers can be stored in variables

p <- ggplot(dsmall, aes(carat, price)) + geom_point() 
p # or print(p)

Returns information about data and aesthetic mappings followed by each layer

summary(p)

Print dots with different sizes and colors

bestfit <- geom_smooth(method = "lm", se = F, color = alpha("steelblue", 0.5), size = 2)
p + bestfit # Plot with custom regression line

Syntax to pass on other data sets

p %+% diamonds[sample(nrow(diamonds), 100),]

Saves plot stored in variable p to file

ggsave(p, file="myplot.pdf")

Standard R export functons for graphics work as well (see here).

`ggplot`: scatter plots

Basic example

set.seed(1410)  
dsmall <- as.data.frame(diamonds[sample(nrow(diamonds), 1000), ])
p <- ggplot(dsmall, aes(carat, price, color=color)) + 
            geom_point(size=4)
print(p)

Interactive version of above plot can be generated with the ggplotly function from the plotly package.

library(plotly)
ggplotly(p)

Regression line

p <- ggplot(dsmall, aes(carat, price)) + geom_point() + 
            geom_smooth(method="lm", se=FALSE) +
            theme(panel.background=element_rect(fill = "white", colour = "black"))
print(p)

## `geom_smooth()` using formula 'y ~ x'

Several regression lines

p <- ggplot(dsmall, aes(carat, price, group=color)) + 
            geom_point(aes(color=color), size=2) + 
            geom_smooth(aes(color=color), method = "lm", se=FALSE) 
print(p)

## `geom_smooth()` using formula 'y ~ x'

Local regression curve (loess)

p <- ggplot(dsmall, aes(carat, price)) + geom_point() + geom_smooth() 
print(p) # Setting se=FALSE removes error shade

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

`ggplot`: line plot

p <- ggplot(iris, aes(Petal.Length, Petal.Width, group=Species, 
            color=Species)) + geom_line() 
print(p)

Faceting

p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
            geom_line(aes(color=Species), size=1) + 
            facet_wrap(~Species, ncol=1)
print(p)

Exercise 3

Scatter plots with ggplot2

Task 1: Generate scatter plot for first two columns in iris data frame and color dots by its Species column.
Task 2: Use the xlim and ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.
Task 3: Generate corresponding line plot with faceting presenting the individual data sets in saparate plots.

Structure of iris data set

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Bar Plots

Sample Set: the following transforms the iris data set into a ggplot2-friendly format.

Calculate mean values for aggregates given by Species column in iris data set

iris_mean <- aggregate(iris[,1:4], by=list(Species=iris$Species), FUN=mean)

Calculate standard deviations for aggregates given by Species column in iris data set

iris_sd <- aggregate(iris[,1:4], by=list(Species=iris$Species), FUN=sd)

Reformat iris_mean with melt from wide to long form as expected by ggplot2. Newer alternatives for restructuring data.frames and tibbles from wide into long form use the gather and pivot_longer functions defined by the tidyr package. Their usage is shown below as well. The functions pivot_longer and pivot_wider are expected to provide the most flexible long-term solution, but may not work in older R versions.

library(reshape2) # Defines melt function
df_mean <- melt(iris_mean, id.vars=c("Species"), variable.name = "Samples", value.name="Values")
df_mean2 <- tidyr::gather(iris_mean, !Species, key = "Samples", value = "Values")
df_mean3 <- tidyr::pivot_longer(iris_mean, !Species, names_to="Samples", values_to="Values")

Reformat iris_sd with melt

df_sd <- melt(iris_sd, id.vars=c("Species"), variable.name = "Samples", value.name="Values")

Define standard deviation limits

limits <- aes(ymax = df_mean[,"Values"] + df_sd[,"Values"], ymin=df_mean[,"Values"] - df_sd[,"Values"])

Verical orientation

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
            geom_bar(position="dodge", stat="identity")
print(p)

To enforce that the bars are plotted in the order specified in the input data, one can instruct ggplot to do so by turning the corresponding column (here Species) into an ordered factor as follows.

df_mean$Species <- factor(df_mean$Species, levels=unique(df_mean$Species), ordered=TRUE)

In the above example this is not necessary since ggplot uses this order already.

Horizontal orientation

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
            geom_bar(position="dodge", stat="identity") + coord_flip() + 
            theme(axis.text.y=element_text(angle=0, hjust=1))
print(p)

Faceting

p <- ggplot(df_mean, aes(Samples, Values)) + geom_bar(aes(fill = Species), stat="identity") +
            facet_wrap(~Species, ncol=1)
print(p)

Error bars

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
            geom_bar(position="dodge", stat="identity") + 
            geom_errorbar(limits, position="dodge") 
print(p)

Mirrored

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
p <- ggplot(df, aes(x=x, y=y, fill=group)) + 
            geom_bar(stat="identity", position="identity")
print(p)

Changing Color Settings

library(RColorBrewer)
# display.brewer.all() 
p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) +
            geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") + 
            scale_fill_brewer(palette="Blues") + scale_color_brewer(palette = "Greys") 
print(p)

Using standard R color theme

p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) + 
            geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") + 
            scale_fill_manual(values=c("red", "green3", "blue")) + 
            scale_color_manual(values=c("red", "green3", "blue")) 
print(p)

Exercise 4

Bar plots

Task 1: Calculate the mean values for the Species components of the first four columns in the iris data set. Use the melt function from the reshape2 package to bring the data into the expected format for ggplot.
Task 2: Generate two bar plots: one with stacked bars and one with horizontally arranged bars.

Structure of iris data set

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Data reformatting example

Here for line plot

y <- matrix(rnorm(500), 100, 5, dimnames=list(paste("g", 1:100, sep=""), paste("Sample", 1:5, sep="")))
y <- data.frame(Position=1:length(y[,1]), y)
y[1:4, ] # First rows of input format expected by melt()

##    Position    Sample1    Sample2    Sample3    Sample4     Sample5
## g1        1  1.5336975 -1.0365027 -2.0276195 -0.4580396 -0.06460952
## g2        2 -2.0960304  2.1878704  0.7260334  0.8274617  0.24192162
## g3        3 -0.8233125  0.4250477  0.6526331 -0.4509962 -1.06778801
## g4        4  1.0961555  0.8101104 -0.3403762 -0.7222191 -0.72737741

df <- melt(y, id.vars=c("Position"), variable.name = "Samples", value.name="Values")
p <- ggplot(df, aes(Position, Values)) + geom_line(aes(color=Samples)) + facet_wrap(~Samples, ncol=1)
print(p)

Same data can be represented in box plot as follows

ggplot(df, aes(Samples, Values, fill=Samples)) + geom_boxplot() + geom_jitter(color="darkgrey")

Jitter Plots

p <- ggplot(dsmall, aes(color, price/carat)) + 
            geom_jitter(alpha = I(1 / 2), aes(color=color))
print(p)

Box plots

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot()
print(p)

Violin plots

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_violin()                                                                                                      
print(p)

Same violin plot as interactive plot generated with ggplotly, where the actual data points are shown as well by including geom_jitter().

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_violin() + geom_jitter(aes(color=color))
ggplotly(p)

Density plots

Line coloring

p <- ggplot(dsmall, aes(carat)) + geom_density(aes(color = color))
print(p)

Area coloring

p <- ggplot(dsmall, aes(carat)) + geom_density(aes(fill = color))
print(p)

Histograms

p <- ggplot(iris, aes(x=Sepal.Width)) + 
            geom_histogram(aes(y = ..density.., fill = ..count..), binwidth=0.2) + 
            geom_density()  
print(p)

Pie Chart

df <- data.frame(variable=rep(c("cat", "mouse", "dog", "bird", "fly")), 
                 value=c(1,3,3,4,2)) 
p <- ggplot(df, aes(x = "", y = value, fill = variable)) + 
            geom_bar(width = 1, stat="identity") + 
            coord_polar("y", start=pi / 3) + ggtitle("Pie Chart") 
print(p)

Wind Rose Pie Chart

p <- ggplot(df, aes(x = variable, y = value, fill = variable)) + 
            geom_bar(width = 1, stat="identity") + 
            coord_polar("y", start=pi / 3) + 
            ggtitle("Pie Chart") 
print(p)

Arranging Graphics on Page

Using grid package

library(grid)
a <- ggplot(dsmall, aes(color, price/carat)) + geom_jitter(size=4, alpha = I(1 / 1.5), aes(color=color))
b <- ggplot(dsmall, aes(color, price/carat, color=color)) + geom_boxplot()
c <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot() + theme(legend.position = "none")
grid.newpage() # Open a new page on grid device
pushViewport(viewport(layout = grid.layout(2, 2))) # Assign to device viewport with 2 by 2 grid layout 
print(a, vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
print(b, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
print(c, vp = viewport(layout.pos.row = 2, layout.pos.col = 2, width=0.3, height=0.3, x=0.8, y=0.8))

Using gridExtra package

library(gridExtra)                                                                                                                                                            
grid.arrange(a, b, c, nrow = 2, ncol=2)

Also see patchwork in ggplot2 book here.

Inserting Graphics into Plots

library(grid)
print(a)
print(b, vp=viewport(width=0.3, height=0.3, x=0.8, y=0.8))

Interactive Graphics with `plotly`

Many graphics generated with ggplot can be rendered into interactive plots using plotly::ggplotly(). Note, interactive graphics can be embedded into HTML pages and viewed in a browser.

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_violin() + geom_jitter(aes(color=color))
ggplotly(p)

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

Specialty Graphics

Spatial Heatmap Diagrams

Spatial expression data can be visualized with the spatialHeatmap package.

[ Scroll down to continue ]

Venn Diagrams

library(systemPipeR)
setlist5 <- list(A=sample(letters, 18), B=sample(letters, 16), C=sample(letters, 20), D=sample(letters, 22), E=sample(letters, 18))
OLlist5 <- overLapper(setlist=setlist5, sep="_", type="vennsets")
vennPlot(OLlist5, mymain="", mysub="default", colmode=2, ccol=c("blue", "red"))

Compound Structures

Plots depictions of small molecules with ChemmineR package

library(ChemmineR)
data(sdfsample)
plot(sdfsample[1], print=FALSE)

Heatmaps

There are many packages for plotting heatmaps in R. The following uses pheatmap.

library(pheatmap); library("RColorBrewer")
y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep="")))
pheatmap(y, color=brewer.pal(9,"Blues"))

ROC Plots

A variety of libraries are available for plotting receiver operating characteristic (ROC) curves in R:

Example

Most commonly, in an ROC we plot the true positive rate (y-axis) against the false positive rate (x-axis) at decreasing thresholds. An illustrative example is provided in the ROCR package where one wants to inspect the content of the ROCR.simple object defining the structure of the input data in two vectors.

# install.packages("ROCR") # Install if necessary
library(ROCR)
data(ROCR.simple)
ROCR.simple

## $predictions
##   [1] 0.612547843 0.364270971 0.432136142 0.140291078 0.384895941 0.244415489 0.970641299 0.890172812 0.781781371 0.868751832 0.716680598 0.360168796 0.547983407 0.385240464 0.423739359 0.101699993 0.628095575
##  [18] 0.744769966 0.657732644 0.490119891 0.072369921 0.172741714 0.105722115 0.890078186 0.945548941 0.984667270 0.360180429 0.448687336 0.014823599 0.543533783 0.292368449 0.701561487 0.715459280 0.714985914
##  [35] 0.120604738 0.319672178 0.911723615 0.757325590 0.090988280 0.529402244 0.257402979 0.589909284 0.708412104 0.326672910 0.086546283 0.879459891 0.362693564 0.230157183 0.779771989 0.876086217 0.353281048
##  [52] 0.212014560 0.703293499 0.689075677 0.627012496 0.240911145 0.402801992 0.134794140 0.120473353 0.665444679 0.536339509 0.623494622 0.885179651 0.353777439 0.408939895 0.265686095 0.932159806 0.248500489
##  [69] 0.858876675 0.491735594 0.151350957 0.694457482 0.496513160 0.123504905 0.499788081 0.310718619 0.907651100 0.340078180 0.195097957 0.371936985 0.517308606 0.419560072 0.865639036 0.018527600 0.539086009
##  [86] 0.005422562 0.772728821 0.703885141 0.348213542 0.277656869 0.458674210 0.059045866 0.133257805 0.083685883 0.531958184 0.429650397 0.717845453 0.537091350 0.212404891 0.930846938 0.083048377 0.468610247
## [103] 0.393378108 0.663367560 0.349540913 0.194398425 0.844415442 0.959417835 0.211378771 0.943432189 0.598162949 0.834803976 0.576836208 0.380396459 0.161874325 0.912325837 0.642933593 0.392173971 0.122284044
## [120] 0.586857799 0.180631658 0.085993218 0.700501359 0.060413627 0.531464015 0.084254795 0.448484671 0.938583020 0.531006532 0.785213140 0.905121019 0.748438143 0.605235403 0.842974300 0.835981859 0.364288579
## [137] 0.492596896 0.488179708 0.259278968 0.991096434 0.757364019 0.288258273 0.773336236 0.040906997 0.110241034 0.760726142 0.984599159 0.253271061 0.697235328 0.620501132 0.814586047 0.300973098 0.378092079
## [154] 0.016694412 0.698826511 0.658692553 0.470206008 0.501489336 0.239143340 0.050999138 0.088450984 0.107031842 0.746588080 0.480100183 0.336592126 0.579511087 0.118555284 0.233160827 0.461150807 0.370549294
## [171] 0.770178504 0.537336015 0.463227453 0.790240205 0.883431431 0.745110673 0.007746305 0.012653524 0.868331219 0.439399995 0.540221346 0.567043171 0.035815400 0.806543942 0.248707470 0.696702150 0.081439129
## [188] 0.336315317 0.126480399 0.636728451 0.030235062 0.268138293 0.983494405 0.728536415 0.739554341 0.522384507 0.858970526 0.383807972 0.606960209 0.138387070
## 
## $labels
##   [1] 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 1
## [108] 0 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0

pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)
perf <- performance( pred, "tpr", "fpr" )
plot(perf)

Obtain area under the curve (AUC)

auc <- performance( pred, "tpr", "fpr", measure = "auc")                                                                                                                      
auc@y.values[[1]]

## [1] 0.8341875

Trees

The ape package provides many useful utilities for phylogenetic analysis and tree plotting. Another useful package for plotting trees is ggtree. The following example plots two trees face to face with links to identical leaf labels.

library(ape)

## 
## Attaching package: 'ape'

## The following object is masked from 'package:ShortRead':
## 
##     zoom

## The following object is masked from 'package:Biostrings':
## 
##     complement

tree1 <- rtree(40)
tree2 <- rtree(20)
association <- cbind(tree2$tip.label, tree2$tip.label)
cophyloplot(tree1, tree2, assoc = association,
            length.line = 4, space = 28, gap = 3)

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

Genome Graphics

`ggbio`

What is ggbio?
- A programmable genome browser environment
Genome broswer concepts
- A genome browser is a visulalization tool for plotting different types of genomic data in separate tracks along chromosomes.
- The ggbio package [@Yin2012-jj] facilitates plotting of complex genome data objects, such as read alignments (SAM/BAM), genomic context/annotation information (gff/txdb), variant calls (VCF/BCF), and more. To easily compare these data sets, it extends the faceting facility of ggplot2 to genome browser-like tracks.
- Most of the core object types for handling genomic data with R/Bioconductor are supported: GRanges, GAlignments, VCF, etc. For more details, see Table 1.1 of the ggbio vignette here.
- ggbio’s convenience plotting function is autoplot. For more customizable plots, one can use the generic ggplot function.
  [ Scroll down to continue ]
- Apart from the standard ggplot2 plotting components, ggbio defines serval new components useful for genomic data visualization. A detailed list is given in Table 1.2 of the vignette here.
- Useful web sites:

Tracks: aligning plots along chromosomes

library(ggbio)
df1 <- data.frame(time = 1:100, score = sin((1:100)/20)*10)
p1 <- qplot(data = df1, x = time, y = score, geom = "line")
df2 <- data.frame(time = 30:120, score = sin((30:120)/20)*10, value = rnorm(120-30 +1))
p2 <- ggplot(data = df2, aes(x = time, y = score)) + geom_line() + geom_point(size = 2, aes(color = value))
tracks(time1 = p1, time2 = p2) + xlim(1, 40) + theme_tracks_sunset()

Plotting genomic ranges

GRanges objects are essential for storing alignment or annotation ranges in R/Bioconductor. The following creates a sample GRanges object and plots its content.

library(GenomicRanges)
set.seed(1); N <- 100; gr <- GRanges(seqnames = sample(c("chr1", "chr2", "chr3"), size = N, replace = TRUE), IRanges(start = sample(1:300, size = N, replace = TRUE), width = sample(70:75, size = N,replace = TRUE)), strand = sample(c("+", "-"), size = N, replace = TRUE), value = rnorm(N, 10, 3), score = rnorm(N, 100, 30), sample = sample(c("Normal", "Tumor"), size = N, replace = TRUE), pair = sample(letters, size = N, replace = TRUE))
autoplot(gr, aes(color = strand, fill = strand), facets = strand ~ seqnames)

Plotting coverage

autoplot(gr, aes(color = strand, fill = strand), facets = strand ~ seqnames, stat = "coverage")

## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.

Mirrored coverage

pos <- sapply(coverage(gr[strand(gr)=="+"]), as.numeric)
pos <- data.frame(Chr=rep(names(pos), sapply(pos, length)), Strand=rep("+", length(unlist(pos))), Position=unlist(sapply(pos, function(x) 1:length(x))), Coverage=as.numeric(unlist(pos)))
neg <- sapply(coverage(gr[strand(gr)=="-"]), as.numeric)
neg <- data.frame(Chr=rep(names(neg), sapply(neg, length)), Strand=rep("-", length(unlist(neg))), Position=unlist(sapply(neg, function(x) 1:length(x))), Coverage=-as.numeric(unlist(neg)))
covdf <- rbind(pos, neg)
p <- ggplot(covdf, aes(Position, Coverage, fill=Strand)) + 
            geom_col() + 
            facet_wrap(~Chr)
p

Circular genome plots

ggplot(gr) + 
    layout_circle(aes(fill = seqnames), geom = "rect")

More complex circular example

seqlengths(gr) <- c(400, 500, 700)
values(gr)$to.gr <- gr[sample(1:length(gr), size = length(gr))]
idx <- sample(1:length(gr), size = 50)
gr <- gr[idx]
ggplot() + 
    layout_circle(gr, geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3) +
    layout_circle(gr, geom = "bar", radius = 10, trackWidth = 4, aes(fill = score, y = score)) +
    layout_circle(gr, geom = "point", color = "red", radius = 14, trackWidth = 3, grid = TRUE, aes(y = score)) +
    layout_circle(gr, geom = "link", linked.to = "to.gr", radius = 6, trackWidth = 1)

Alignments and variants

To make the following example work, please download and unpack this data archive containing GFF, BAM and VCF sample files.

library(rtracklayer); library(GenomicFeatures); library(Rsamtools); library(GenomicAlignments); library(VariantAnnotation)
ga <- readGAlignments("./data/SRR064167.fastq.bam", use.names=TRUE, param=ScanBamParam(which=GRanges("Chr5", IRanges(4000, 8000))))
p1 <- autoplot(ga, geom = "rect")
p2 <- autoplot(ga, geom = "line", stat = "coverage")
vcf <- readVcf(file="data/varianttools_gnsap.vcf", genome="ATH1")
p3 <- autoplot(vcf[seqnames(vcf)=="Chr5"], type = "fixed") + xlim(4000, 8000) + theme(legend.position = "none", axis.text.y = element_blank(), axis.ticks.y=element_blank())
txdb <- makeTxDbFromGFF(file="./data/TAIR10_GFF3_trunc.gff", format="gff3")
p4 <- autoplot(txdb, which=GRanges("Chr5", IRanges(4000, 8000)), names.expr = "gene_id")
tracks(Reads=p1, Coverage=p2, Variant=p3, Transcripts=p4, heights = c(0.3, 0.2, 0.1, 0.35)) + ylab("")

Additional examples

See autoplot demo here

Additional genome graphics

Gviz
RCircos [@Zhang2013-zn]
genoPlotR

Genome Browser: IGV

View genome data in IGV

Download and open IGV
Select in menu in top left corner A. thaliana (TAIR10)
Upload the following indexed/sorted Bam files with File -> Load from URL...

https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064154.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064155.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064166.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064167.fastq.bam

To view area of interest, enter its coordinates Chr1:49,457-51,457 in position menu on top.

Create symbolic links

For viewing BAM files in IGV as part of systemPipeR workflows.

systemPipeR: utilities for building NGS analysis pipelines.

library("systemPipeR")
symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), 
            urlbase="http://myserver.edu/~username/",
            urlfile="IGVurl.txt")

Controlling IGV from R

Open IGV before running the following routine. Alternatively, open IGV from within R with startIGV("lm") . Note this may not work on all systems.

library(SRAdb)
myurls <- readLines("http://cluster.hpcc.ucr.edu/~tgirke/Documents/R_BioCond/Samples/bam_urls.txt")
#startIGV("lm") # opens IGV
sock <- IGVsocket()
session <- IGVsession(files=myurls, 
                      sessionFile="session.xml", 
                      genome="A. thaliana (TAIR10)")
IGVload(sock, session)
IGVgoto(sock, 'Chr1:45296-47019')

Outline

Overview
Base Graphics
Grid Graphics
Graphics with lattice
Graphics with ggplot2 (ggplotly)
Specialty Graphics
Genome Graphics
References

How to Navigate this Slide Show?

Outline

Overview

Graphics in R

Documentation on Graphics in R

Graphics Environments

Outline

Base Graphics

Overview

Preferred Input Data Objects

Scatter Plots

Basic scatter plots

All pairs

Plot labels

More examples

Exercise 1

Line Plots

Single Data Set

Many Data Sets

Bar Plots

Basics

Error bars

Mirrored bar plot

Histograms

Density Plots}

Pie Charts

Color Selection Utilities

Arranging Several Plots on Single Page

Arranging Plots with Variable Width

Saving Graphics to Files

Exercise 2

Outline

Grid Graphics

Outline

lattice Graphics

Scatter Plot Sample

Line Plot Sample

Outline

ggplot2 Graphics

Design Concept of ggplot2

ggplot2 Usage

Usage with qplot Function

qplot: scatter plot basics

Colored groups

Regression line

Local regression curve (loess)

Usage with ggplot Function

Changing Plotting Themes in ggplot

Storing ggplot Specifications

ggplot: scatter plots

Basic example

Regression line

Several regression lines

Local regression curve (loess)

ggplot: line plot

Faceting

Exercise 3

Bar Plots

Verical orientation

Horizontal orientation

Faceting

Error bars

Mirrored

Changing Color Settings

Using standard R color theme

Exercise 4

Data reformatting example

Jitter Plots

Box plots

Violin plots

Density plots

Line coloring

Area coloring

Histograms

Pie Chart

Wind Rose Pie Chart

Arranging Graphics on Page

Inserting Graphics into Plots

Interactive Graphics with plotly

Outline

Design Concept of `ggplot2`

`ggplot2` Usage

Usage with `qplot` Function

`qplot`: scatter plot basics

Usage with `ggplot` Function

Changing Plotting Themes in `ggplot`

Storing `ggplot` Specifications

`ggplot`: scatter plots

`ggplot`: line plot

Interactive Graphics with `plotly`

`ggbio`